I’m hoping to get some clarification on numerically solving Bellman equations.
So in the McCall sequential search model (https://lectures.quantecon.org/py/mccall_model_with_separation.html) we just directly go about trying to numerically solve the Bellman’s at hand using the CMP.
But once we get ahead of that (for example Optimal growth https://lectures.quantecon.org/py/optgrowth.html) we start using linear interpolation. I’m not exactly clear about what makes the difference here? What part of the model requires us to put in linear interpolation in one of the models but not the other? I am not able to fully get a grasp on this.
Additionally, just another related clarfication while I have you here: the value function which we input essentially just a vector of numbers each representing the value under a certain state right? So if my V = [v_1, v_2, v_3] then v_i is the value that I’d get under outcome i which has a probability (lets say) p_i?
Thanks. And sorry. I seem to be very confused about the fundamentals here which I once thought i was clear about